视差信息引导的光场特征匹配度量方法(特邀)

Disparity-Guided Matching Metric Method for Light Field Features(Invited)

现有图像特征匹配方法在应对光照变化、几何形变等复杂场景时仍具有较大局限性,原因在于特征匹配的度量缺乏深度信息与全局约束。针对该问题,提出一种视差信息引导的光场特征匹配度量方法。对光场数据应用傅里叶视差层分解以构建尺度-视差空间,从而提取包含视差信息的光场特征。依据不同视角光场特征的投影变换关系模型,构建了一种依赖光场深度线索的特征匹配度量模型。采用人工神经网络学习投影变换模型参数的求解方法以重投影误差最小化为目标函数,采用迭代优化方式实现最优投影变换模型的高精度求解,并最终实现对特征点匹配的精确度量。在光场特征匹配数据集上的实验结果表明,相较于现有主流特征匹配方法,针对存在光照变化、几何变形、非朗伯反射表面、重复纹理且具有显著深度变化的场景,所提视差信息引导的光场特征匹配度量模型取得了更优的匹配准确度与鲁棒性。

The existing image feature matching methods still have significant limitations in dealing with complex scenes such as lighting changes and geometric deformations,due to the lack of depth information and global constraints in the measurement of feature matching.A method for measuring light field feature matching guided by disparity information is proposed to address this issue.Applying Fourier disparity layer decomposition to light field data to construct a scale disparity space,in order to extract light field features containing disparity information.A feature matching metric model that relies on depth cues of light fields was constructed based on projection transformation relationship models of light field features from different perspectives.The method of using artificial neural networks to learn the parameters of the projection transformation model aims to minimize the reprojection error as the objective function,and uses iterative optimization to achieve high-precision solution of the optimal projection transformation model,ultimately achieving the accuracy of feature point matching.The experimental results on the light field feature matching dataset show that compared to existing mainstream feature matching methods,the proposed disparity-guided light field feature matching metric model achieves better matching accuracy and robustness for scenes with lighting changes,geometric deformations,non Lambertian reflective surfaces,repetitive textures,and significant depth changes.